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Current Computer-Aided Drug Design


ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Clustering of Zika Viruses Originating from Different Geographical Regions using Computational Sequence Descriptors

Author(s): Marjan Vračko*, Subhash C. Basak, Dwaipayan Sen and Ashesh Nandy

Volume 17, Issue 2, 2021

Published on: 26 December, 2019

Page: [314 - 322] Pages: 9

DOI: 10.2174/1573409916666191226110936

Price: $65


Background: In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens).

Methods: For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods.

Results: Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods.

Conclusion: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Keywords: Zika virus, geographical distribution, clustering, self-organizing map, principal component analysis, alignment-freedescriptor, mahalanobis distance.

Graphical Abstract
Buchman, A.L.; Scolapio, J.; Fryer, J. AGA technical review on short bowel syndrome and intestinal transplantation. Gastroenterology, 2003, 124(4), 1111-1134.
[] [PMID: 12671904]
Misiakos, E.P.; Macheras, A.; Kapetanakis, T.; Liakakos, T. Short bowel syndrome: current medical and surgical trends. J. Clin. Gastroenterol., 2007, 41(1), 5-18.
[] [PMID: 17198059]
O'Keefe, S.J.; Buchman, A.L.; Fishbein, T.M.; Jeejeebhoy, K.N.; Jeppesen, P.B.; Shaffer, J. Short bowel syndrome and intestinal failure: consensus definitions and overview., Clin. Gastroenterol. Hepatol. : the official clinical practice journal of the American Gastroenterological Association, 2006, 4, 6-10..
McMellen, M.E.; Wakeman, D.; Longshore, S.W.; McDuffie, L.A.; Warner, B.W. Growth factors: possible roles for clinical management of the short bowel syndrome. Semin. Pediatr. Surg., 2010, 19(1), 35-43.
[] [PMID: 20123272]
Scolapio, J.S. Short bowel syndrome: recent clinical outcomes with growth hormone. Gastroenterology, 2006, 130(2)(Suppl. 1), S122-S126.
[] [PMID: 16473059]
Seidner, D.L.; Schwartz, L.K.; Winkler, M.F.; Jeejeebhoy, K.; Boullata, J.I.; Tappenden, K.A. Increased intestinal absorption in the era of teduglutide and its impact on management strategies in patients with short bowel syndrome-associated intestinal failure. JPEN J. Parenter. Enteral Nutr., 2013, 37(2), 201-211.
[] [PMID: 23343999]
Burness, C.B.; McCormack, P.L. Teduglutide: a review of its use in the treatment of patients with short bowel syndrome. Drugs, 2013, 73(9), 935-947.
[] [PMID: 23729002]
Brubaker, P.L.; Crivici, A.; Izzo, A.; Ehrlich, P.; Tsai, C.H.; Drucker, D.J. Circulating and tissue forms of the intestinal growth factor, glucagon-like peptide-2. Endocrinology, 1997, 138(11), 4837-4843.
[] [PMID: 9348213]
Drucker, D.J.; Shi, Q.; Crivici, A.; Sumner-Smith, M.; Tavares, W.; Hill, M.; DeForest, L.; Cooper, S.; Brubaker, P.L. Regulation of the biological activity of glucagon-like peptide 2 in vivo by dipeptidyl peptidase IV. Nat. Biotechnol., 1997, 15(7), 673-677.
[] [PMID: 9219272]
Wallis, K.; Walters, J.R.; Gabe, S. Short bowel syndrome: the role of GLP-2 on improving outcome. Curr. Opin. Clin. Nutr. Metab. Care, 2009, 12(5), 526-532.
[] [PMID: 19474717]
Guan, X. The CNS glucagon-like peptide-2 receptor in the control of energy balance and glucose homeostasis. Am. J. Physiol. Regul. Integr. Comp. Physiol., 2014, 307(6), R585-R596.
[] [PMID: 24990862]
Hornby, P.J.; Moore, B.A. The therapeutic potential of targeting the glucagon-like peptide-2 receptor in gastrointestinal disease. Expert Opin. Ther. Targets, 2011, 15(5), 637-646.
[] [PMID: 21314232]
Runge, S.; Thøgersen, H.; Madsen, K.; Lau, J.; Rudolph, R. Crystal structure of the ligand-bound glucagon-like peptide-1 receptor extracellular domain. J. Biol. Chem., 2008, 283(17), 11340-11347.
[] [PMID: 18287102]
Macalino, S.J.; Gosu, V.; Hong, S.; Choi, S. Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res., 2015, 38(9), 1686-1701.
[] [PMID: 26208641]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[] [PMID: 24381236]
Wang, T.; Wu, M.B.; Lin, J.P.; Yang, L.R. Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expert Opin. Drug Discov., 2015, 10(12), 1283-1300.
[] [PMID: 26358617]
Wang, X.; Chen, H.; Yang, F.; Gong, J.; Li, S.; Pei, J.; Liu, X.; Jiang, H.; Lai, L.; Li, H. iDrug: a web-accessible and interactive drug discovery and design platform. J. Cheminform., 2014, 6, 28.
[] [PMID: 24955134]
Gesto, D.S.; Cerqueira, N.M.; Ramos, M.J.; Fernandes, P.A. Discovery of new druggable sites in the anti-cholesterol target HMG-CoA reductase by computational alanine scanning mutagenesis. J. Mol. Model., 2014, 20(4), 2178.
[] [PMID: 24671303]
Moal, I.H.; Jiménez-García, B.; Fernández-Recio, J. CCharPPI web server: computational characterization of protein-protein interactions from structure. Bioinformatics, 2015, 31(1), 123-125.
[] [PMID: 25183488]
Ramos, R.M.; Moreira, I.S. Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes. J. Chem. Theory Comput., 2013, 9(9), 4243-4256.
[] [PMID: 26592413]
Sukhwal, A.; Sowdhamini, R. PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots. Bioinform. Biol. Insights, 2015, 9, 141-151.
[] [PMID: 26448684]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[] [PMID: 10592235]
DaCambra, M.P.; Yusta, B.; Sumner-Smith, M.; Crivici, A.; Drucker, D.J.; Brubaker, P.L. Structural determinants for activity of glucagon-like peptide-2. Biochemistry, 2000, 39(30), 8888-8894.
[] [PMID: 10913301]
Arnold, K.; Bordoli, L.; Kopp, J.; Schwede, T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics, 2006, 22(2), 195-201.
[] [PMID: 16301204]
Kiefer, F.; Arnold, K.; Künzli, M.; Bordoli, L.; Schwede, T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res., 2009, 37(Database issue), D387-D392.
[] [PMID: 18931379]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[] [PMID: 16200636]
Salomon-Ferrer, R.; Case, D.A.; Walker, R.C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2013, 3, 198-210.
Venneti, K.C.; Hewage, C.M. Conformational and molecular interaction studies of glucagon-like peptide-2 with its N-terminal extracellular receptor domain. FEBS Lett., 2011, 585(2), 346-352.
[] [PMID: 21167157]
Laimer, J.; Hiebl-Flach, J.; Lengauer, D.; Lackner, P. MAESTROweb: a web server for structure-based protein stability prediction. Bioinformatics, 2016, 32(9), 1414-1416.
[] [PMID: 26743508]
Laimer, J.; Hofer, H.; Fritz, M.; Wegenkittl, S.; Lackner, P. MAESTRO--multi agent stability prediction upon point mutations. BMC Bioinformatics, 2015, 16, 116.
[] [PMID: 25885774]
Krissinel, E. Crystal contacts as nature’s docking solutions. J. Comput. Chem., 2010, 31(1), 133-143.
[] [PMID: 19421996]
Krissinel, E.; Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol., 2007, 372(3), 774-797.
[] [PMID: 17681537]
Hospital, A.; Goñi, J.R.; Orozco, M.; Gelpí, J.L. Molecular dynamics simulations: advances and applications. Adv. Appl. Bioinform. Chem., 2015, 8, 37-47.
[PMID: 26604800]
Couvineau, A.; Rouyer-Fessard, C.; Laburthe, M. Presence of a N-terminal signal peptide in class II G protein-coupled receptors: crucial role for expression of the human VPAC1 receptor. Regul. Pept., 2004, 123(1-3), 181-185.
[] [PMID: 15518910]
Parthier, C.; Reedtz-Runge, S.; Rudolph, R.; Stubbs, M.T. Passing the baton in class B GPCRs: peptide hormone activation via helix induction? Trends Biochem. Sci., 2009, 34(6), 303-310.
[] [PMID: 19446460]
Buza, K.; Peška, L. Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing, 2017, 260, 284-293.
Buza, K.; Peška, L. A New Approach for Drug–Target Interaction Prediction.Machine Learning and Knowledge Discovery in Databases. In: ECML PKDD 2017. Lecture Notes in Computer Science; Ceci M., H.J.; Todorovski, L.; Vens, C.; Džeroski, S., Eds.; , 2017; 10535, pp. 322-337..
Peska, L.; Buza, K.; Koller, J. Drug-target interaction prediction: a bayesian ranking approach. Comput. Methods Programs Biomed., 2017, 152, 15-21.
[] [PMID: 29054256]
Abbasi, W.A.; Asif, A.; Ben-Hur, A.; Minhas, F.U.A.A. Learning protein binding affinity using privileged information. BMC Bioinformatics, 2018, 19(1), 425.
[] [PMID: 30442086]

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